The present invention relates to the processing of resistivity logs, and more particularly to a method of correcting the log measurements for borehole effects using a neural-net (NN) implementation. This makes it possible to obtain rapid correction for borehole effects prior to inversion of the resistivity data.
A commonly used technique for evaluating formations surrounding an earth borehole is resistivity logging. Porous formations having high resistivity generally indicate the presence of hydrocarbons, while porous formations with low resistivity are generally water saturated. There are many prior art methods for the determination of the resistivity of subsurface earth formations using resistivity logging tools.
The physical principles of electromagnetic induction resistivity well logging are described, for example, in, H. G. Doll, Introduction to Induction Logging and Application to Logging of Wells Drilled with Oil Based Mud, Journal of Petroleum Technology, vol. 1, p.148, Society of Petroleum Engineers, Richardson Tex. (1949). Many improvements and modifications to electromagnetic induction resistivity instruments have been devised since publication of the Doll reference, supra. Examples of such modifications and improvements can be found, for example, in U.S. Pat. No. 4,837,517, U.S. Pat. No. 5,157,605 issued to Chandler et al, and U.S. Pat. No. 5,452,761 issued to Beard et al.
A limitation to the electromagnetic induction resistivity well logging instruments known in the art is that they typically include transmitter coils and receiver coils wound so that the magnetic moments of these coils are substantially parallel only to the axis of the instrument. Eddy currents are induced in the earth formations from the magnetic field generated by the transmitter coil, and in the induction instruments known in the art these eddy currents tend to flow in ground loops which are substantially perpendicular to the axis of the instrument. Voltages are then induced in the receiver coils related to the magnitude of the eddy currents. Certain earth formations, however, consist of thin layers of electrically conductive materials interleaved with thin layers of substantially non-conductive material. The response of the typical electromagnetic induction resistivity well logging instrument will be largely dependent on the conductivity of the conductive layers when the layers are substantially parallel to the flow path of the eddy currents. The substantially non-conductive layers will contribute only a small amount to the overall response of the instrument and therefore their presence will typically be masked by the presence of the conductive layers. The non-conductive layers, however, are the ones which are typically hydrocarbon-bearing and are of the most interest to the instrument user. Some earth formations which might be of commercial interest therefore may be overlooked by interpreting a well log made using the electromagnetic induction resistivity well logging instruments known in the art.
U.S. Pat. No. 5,999,883 issued to Gupta et al, (the xe2x80x9cGupta patentxe2x80x9d), the contents of which are fully incorporated here by reference, discloses a method for determination of the horizontal and vertical conductivity of anisotropic earth formations. Electromagnetic induction signals induced by induction transmitters oriented along three mutually orthogonal axes are measured. One of the mutually orthogonal axes is substantially parallel to a logging instrument axis. The electromagnetic induction signals are measured using first receivers each having a magnetic moment parallel to one of the orthogonal axes and using second receivers each having a magnetic moment perpendicular to a one of the orthogonal axes which is also perpendicular to the instrument axis. A relative angle of rotation of the perpendicular one of the orthogonal axes is calculated from the receiver signals measured perpendicular to the instrument axis. An intermediate measurement tensor is calculated by rotating magnitudes of the receiver signals through a negative of the angle of rotation. A relative angle of inclination of one of the orthogonal axes which is parallel to the axis of the instrument is calculated, from the rotated magnitudes, with respect to a direction of the vertical conductivity. The rotated magnitudes are rotated through a negative of the angle of inclination. Horizontal conductivity is calculated from the magnitudes of the receiver signals after the second step of rotation. An anisotropy parameter is calculated from the receiver signal magnitudes after the second step of rotation. Vertical conductivity is calculated from the horizontal conductivity and the anisotropy parameter.
One problem with the inversion of electromagnetic data is that the region immediately surrounding the borehole can be invaded by borehole fluid or mud filtrate and have a different resistivity than the virgin formation. This turns what is a 1-D inversion into a 2-D inversion. To deal with this problem, it is common practice to use resistivity logging devices with multiple depths of investigation to provide information about the properties of the virgin formation, the invaded zone and the borehole.
In measurements made at low frequencies using induction logging instruments in a vertical borehole, the borehole effects add linearly to the tool response. For such a situation, the borehole correction may be simply applied by simply considering the response of a fluid-filled borehole in a homogenous formation. After applying the borehole correction, a straighforward inversion of the borehole-corrected data readily gives a layered model of the formations surrounding the borehole.
In measurements made with galvanic instruments, induction logging tools at nonzero frequencies or propagation resistivity tools at nonzero frequencies, the borehole effect is no longer additive. In these cases, the problem becomes nonlinear and the borehole corrections become a function of the properties of the formation in addition to the properties of the borehole. The fundamental reason for the nonlinearity in all of these situations is the accumulation of electrical charges at the interfaces between layers of the formation. Accordingly, this problem of nonlinearity also arises at low frequencies in induction logging of deviated boreholes wherein the axis of the borehole is not normal to the bedding planes in the formation and in induction logging using a transverse coil. In crossing the borehole wall, resulting in charge accumulation and the accompanying nonlinearity.
U.S. Pat. No. 5,900,733 to Wu et al. discloses a well logging method and apparatus for determining borehole corrected formation resistivity, borehole diameter, and downhole borehole fluid (mud) resistivity with improved accuracy. A logging device in the borehole transmits electromagnetic energy from a transmitter, which energy is received at receivers on the logging device. The phase and amplitude of the received energy are measured at the receivers and a phase shift, phase average, and attenuation are associated with the transmitter-to-receivers spacing. The process is then repeated for a plurality of further transmitters having different spacings from the receivers. A formation and borehole model having model values of borehole corrected formation resistivity, borehole diameter, and borehole fluid resistivity is obtained by inversion of the measured data. Values of borehole corrected formation resistivity, borehole diameter, and borehole fluid resistivity that would produce a model phase shift, phase average and attenuation corresponding to the measured values of these parameters are then determined.
U.S. Pat. No. 5,867,806 to Strickland et al discloses a method in which one or more control depths at one or more locations of each of a plurality of detected beds in the formation. The control depths are determined based on determined bed boundaries. The method then estimates the resistivity of each bed only at the selected control depths to produce an estimated resistivity of the beds. The method then computes a simulated log value at each control depth using a current estimate of the resistivity of the beds. The computed simulated log, is then compared to the actual log data at each control depth, and the resistivity of each bed is adjusted using the difference between the actual and simulated values at the control depths. The above method iteratively repeats a plurality of times until the simulated log substantially matches the actual log at the control depths.
The prior art methods for correction for borehole effects generally assume simple models for these borehole effects. Typically, the borehole effect is modeled using a single invaded zone and borehole axis is assumed to be normal to the bed boundaries. Even in such a simplified model, the compensation for borehole effects and shoulder bed effects is quite time consuming.
There is a need for a method of correcting resistivity logging data for the effects of fluid invasion and shoulder beds in complicated environments. Such a method should preferably be simple and preferably should be capable of real time implementation, so that resistivity measurements may be corrected for borehole effects prior to further processing. The present invention satisfies this need.
The present invention is a method for borehole correction of resistivity logging data. The method comprises two stages. In the first stage, the entire range of possibilities of earth models relevant to borehole compensation is sampled and a suite of tool responses is generated, with and without the borehole. A wide range tool responses including the borehole effects are input to a neural-net (NN) and the NN is trained to produce the corresponding borehole-free response. Once the NN has been trained ( in terms of a set of weighting coefficients), in the second stage, the NN is validated by using as input tool responses (including borehole effects) that were not used in the training of the NN and comparing the output of the NN to the corresponding borehole-free response. If the agreement is good, then the NN has been validated and may be used to process subsequently acquired data that includes borehole effects. If the agreement is not good, then the NN is retrained with a different sampling of the earth model.